Orthogonal Frequency Division Multiplexing (OFDM), one of the underlying technology in MIMO-OFDM is a multicarrier communication system which converts a high data rate stream into a set of parallel low data rate streams thereby modifying the prior art problem in a wireless channel, namely frequency selective fading to a tractable flat fading. Digital communications using multiple input multiple output (MIMO) wireless links has recently emerged as one of the most promising research areas in wireless communications. The core idea of the MIMO systems is space-time signal processing in which time is complemented with the spatial dimension inherent in the use of multiple spatially distributed antennas, resulting in diversity gain or multiplexing gain or both. An exciting combination explored, to further enhance the bandwidth efficiency and throughput performance, is MIMO-OFDM. Here OFDM is used to convert the frequency selective channel in the conventional MIMO systems into a set of parallel frequency flat channels. Space-time coding is then applied to a group of tones in an OFDM symbol or on a per tone basis across OFDM symbols.
While MIMO-OFDM is robust to frequency selective fading, it is very sensitive to frequency offset caused by Doppler shifts and/or oscillator instabilities like the conventional OFDM systems. The presence of carrier frequency offset (CFO) will introduce severe inter-carrier interference (ICI), which, if not properly compensated, would results in loss of orthogonality and significantly degrade the system performance. The current demand for low-cost receivers make the design of frequency synchronization block more challenging as the amount of frequency offsets need to be 10 estimated and corrected would be in the range of a few multiples of the subcarrier spacing. On the other hand, the receiver complexity and the training overhead have to be kept at a minimum level.
Many techniques are found in state of the art which deal with carrier frequency offset estimation in conventional single input single output (SISO) OFDM systems [1-5]. For example, Schmidl-Cox algorithm [1] employs two training OFDM symbols to achieve an overall frequency estimation range of two subcarrier spacings. A modified forms of Schmidl-Cox algorithm is proposed in [2] where one training symbol with P identical subparts in time domain are used to yield an estimation range of +/−P/2 subcarrier spacings. There is a class of carrier frequency offset estimators which use either the intrinsic virtual carriers present in some of the OFDM based wireless communication standards or deliberately introduced null subcarriers in between the data carriers. They estimate the frequency offset by employing a cost function which minimize the total null subcarrier energy with the help of a global search technique. Liu and Tureli proposed a subspace based frequency offset estimate approach using consecutively placed virtual carriers at the band edges of the OFDM symbol [3]. Ma et al. suggested the use of distributed null subcarriers [4], to minimize the estimation errors associated with the use of consecutively placed virtual subcarriers proposed in [3]. Recently a null subcarrier based method is proposed which uses one complete OFDM symbol with all odd subcarriers and most of the even subcarriers as null subcarriers which are allocated based on an extended PN sequence [5].
But only a few methods are available which exclusively address the CFO estimation in MIMO-OFDM systems. There are various shortcomings in the state of the art methods. Training preamble based frequency offset estimation methods are known [6-8], which are extensions of similar techniques reported for SISO-OFDM like [1-2]. These techniques need large bandwidth overheads in order to send specific training sequences or pilot signals. A few other methods exist which aim at reducing the training overhead but the number of computations required for estimating the CFO are very high [9-11]. Higher computations amounts to higher cost and/or higher latency and both are undesirable properties. Another performance measure of CFO estimation algorithms is the range of frequency offset that they can provide. While the maximum frequency offset estimation range is equal to the OFDM bandwidth, all of the training preamble based estimation techniques provide at the most ⅛th or ¼th of it only. On the contrary, methods which offer very high estimation range are computationally inefficient. An attempt for reducing the computational complexity associated with the CFO estimation is done in [12]. A few granted patents [P.1-P.2] and patent applications [P.3-P.4] also exist in the state of the art. In view of the growing popularity, efficient techniques for the CFO estimation with excellent performances are still needed for practical MIMO_OFDM system implementations.